import torch


class Attention(torch.nn.Module):

    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
        super().__init__()
        dim_inner = head_dim * num_heads
        kv_dim = kv_dim if kv_dim is not None else q_dim
        self.num_heads = num_heads
        self.head_dim = head_dim

        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)

    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states

        batch_size = encoder_hidden_states.shape[0]

        q = self.to_q(hidden_states)
        k = self.to_k(encoder_hidden_states)
        v = self.to_v(encoder_hidden_states)

        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)

        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
        hidden_states = hidden_states.to(q.dtype)

        hidden_states = self.to_out(hidden_states)

        return hidden_states


class CLIPEncoderLayer(torch.nn.Module):
    def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True):
        super().__init__()
        self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True)
        self.layer_norm1 = torch.nn.LayerNorm(embed_dim)
        self.layer_norm2 = torch.nn.LayerNorm(embed_dim)
        self.fc1 = torch.nn.Linear(embed_dim, intermediate_size)
        self.fc2 = torch.nn.Linear(intermediate_size, embed_dim)

        self.use_quick_gelu = use_quick_gelu

    def quickGELU(self, x):
        return x * torch.sigmoid(1.702 * x)
    
    def forward(self, hidden_states, attn_mask=None):
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.attn(hidden_states, attn_mask=attn_mask)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.fc1(hidden_states)
        if self.use_quick_gelu:
            hidden_states = self.quickGELU(hidden_states)
        else:
            hidden_states = torch.nn.functional.gelu(hidden_states)
        hidden_states = self.fc2(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states
    

class FluxTextEncoderClip(torch.nn.Module):
    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072):
        super().__init__()

        # token_embedding
        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)

        # position_embeds (This is a fixed tensor)
        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))

        # encoders
        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])

        # attn_mask
        self.attn_mask = self.attention_mask(max_position_embeddings)

        # final_layer_norm
        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)

    def attention_mask(self, length):
        mask = torch.empty(length, length)
        mask.fill_(float("-inf"))
        mask.triu_(1)
        return mask

    def forward(self, input_ids, clip_skip=2, extra_mask=None):
        embeds = self.token_embedding(input_ids)
        embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device)
        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
        if extra_mask is not None:
            attn_mask[:, extra_mask[0]==0] = float("-inf")
        for encoder_id, encoder in enumerate(self.encoders):
            embeds = encoder(embeds, attn_mask=attn_mask)
            if encoder_id + clip_skip == len(self.encoders):
                hidden_states = embeds
        embeds = self.final_layer_norm(embeds)
        pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
        return pooled_embeds, hidden_states